Heat is a distributed tensor framework for high performance data analytics.
- What is Heat for?
- Features
- Getting Started
- Installation
- Support Channels
- Contribution guidelines
- License
- Citing Heat
- FAQ
- Acknowledgements
Heat builds on PyTorch and mpi4py to provide high-performance computing infrastructure for memory-intensive applications within the NumPy/SciPy ecosystem.
With Heat you can:
- port existing NumPy/SciPy code from single-CPU to multi-node clusters with minimal coding effort;
- exploit the entire, cumulative RAM of your many nodes for memory-intensive operations and algorithms;
- run your NumPy/SciPy code on GPUs (CUDA, ROCm, coming up: Apple MPS).
For a example that highlights the benefits of multi-node parallelism, hardware acceleration, and how easy this can be done with the help of Heat, see, e.g., our blog post on trucated SVD of a 200GB data set.
Check out our coverage tables to see which NumPy, SciPy, scikit-learn functions are already supported.
If you need a functionality that is not yet supported:
- search existing issues and make sure to leave a comment if someone else already requested it;
- open a new issue.
Check out our features and the Heat API Reference for a complete list of functionalities.
- High-performance n-dimensional arrays
- CPU, GPU, and distributed computation using MPI
- Powerful data analytics and machine learning methods
- Seamless integration with the NumPy/SciPy ecosystem
- Python array API (work in progress)
Go to Quick Start for a quick overview. For more details, see Installation.
You can test your setup by running the heat_test.py script:
mpirun -n 2 python heat_test.pyIt should print something like this:
x is distributed: True
Global DNDarray x: DNDarray([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=ht.int32, device=cpu:0, split=0)
Global DNDarray x:
Local torch tensor on rank 0 : tensor([0, 1, 2, 3, 4], dtype=torch.int32)
Local torch tensor on rank 1 : tensor([5, 6, 7, 8, 9], dtype=torch.int32)Our Jupyter Notebook Tutorial illustrates Heat's basics. More tutorials here.
The complete documentation of the latest version is always deployed on Read the Docs.
- python >= 3.8
- MPI (OpenMPI, MPICH, Intel MPI, etc.)
- mpi4py >= 3.0.0
- pytorch >= 1.8.0
- h5py
- netCDF4
In order to do computations on your GPU(s):
- your CUDA or ROCm installation must match your hardware and its drivers;
- your PyTorch installation must be compiled with CUDA/ROCm support.
On most HPC-systems you will not be able to install/compile MPI or CUDA/ROCm yourself. Instead, you will most likely need to load a pre-installed MPI and/or CUDA/ROCm module from the module system. Maybe, you will even find PyTorch, h5py, or mpi4py as (part of) such a module. Note that for optimal performance on GPU, you need to usa an MPI library that has been compiled with CUDA/ROCm support (e.g., so-called "CUDA-aware MPI").
Install the latest version with
pip install heat[hdf5,netcdf]where the part in brackets is a list of optional dependencies. You can omit it, if you do not need HDF5 or NetCDF support.
The conda build includes all dependencies including OpenMPI.
conda install -c conda-forge heatGo ahead and ask questions on GitHub Discussions. If you found a bug or are missing a feature, then please file a new issue. You can also get in touch with us on Mattermost (sign up with your GitHub credentials). Once you log in, you can introduce yourself on the Town Square channel.
We welcome contributions from the community, if you want to contribute to Heat, be sure to review the Contribution Guidelines and Resources before getting started!
We use GitHub issues for tracking requests and bugs, please see Discussions for general questions and discussion. You can also get in touch with us on Mattermost (sign up with your GitHub credentials). Once you log in, you can introduce yourself on the Town Square channel.
If you’re unsure where to start or how your skills fit in, reach out! You can ask us here on GitHub, by leaving a comment on a relevant issue that is already open.
If you are new to contributing to open source, this guide helps explain why, what, and how to get involved.
- David Henty's course
- Wes Kendall's Tutorials
- Rolf Rabenseifner's MPI course material (including C, Fortran and Python via
mpi4py)
Heat is distributed under the MIT license, see our LICENSE file.
Please do mention Heat in your publications if it helped your research. You can cite:
- Götz, M., Debus, C., Coquelin, D., Krajsek, K., Comito, C., Knechtges, P., Hagemeier, B., Tarnawa, M., Hanselmann, S., Siggel, S., Basermann, A. & Streit, A. (2020). HeAT - a Distributed and GPU-accelerated Tensor Framework for Data Analytics. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 276-287). IEEE, DOI: 10.1109/BigData50022.2020.9378050.
@inproceedings{heat2020,
title={{HeAT -- a Distributed and GPU-accelerated Tensor Framework for Data Analytics}},
author={
Markus Götz and
Charlotte Debus and
Daniel Coquelin and
Kai Krajsek and
Claudia Comito and
Philipp Knechtges and
Björn Hagemeier and
Michael Tarnawa and
Simon Hanselmann and
Martin Siggel and
Achim Basermann and
Achim Streit
},
booktitle={2020 IEEE International Conference on Big Data (Big Data)},
year={2020},
pages={276-287},
month={December},
publisher={IEEE},
doi={10.1109/BigData50022.2020.9378050}
}
Work in progress...
This work is supported by the Helmholtz Association Initiative and Networking Fund under project number ZT-I-0003 and the Helmholtz AI platform grant.
This project has received funding from Google Summer of Code (GSoC) in 2022.
